activity
20162022
most citedMulti-class Classification without Multi-class Labels

17 citations · 46 across the 7 of their papers we have counts for

collaborators

12 papers

cs.CV2022★ 8 cited

AdaNeRF: Adaptive Sampling for Real-time Rendering of Neural Radiance Fields

Andreas Kurz, Thomas Neff, Zhaoyang Lv +2

Novel view synthesis has recently been revolutionized by learning neural radiance fields directly from sparse observations. However, rendering images with this new paradigm is slow…

cs.CV2021★ 2 cited

LiveView: Dynamic Target-Centered MPI for View Synthesis

Sushobhan Ghosh, Zhaoyang Lv, Nathan Matsuda +3

Existing Multi-Plane Image (MPI) based view-synthesis methods generate an MPI aligned with the input view using a fixed number of planes in one forward pass. These methods produce…

cs.CV2020★ 1 cited

STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering

Wentao Yuan, Zhaoyang Lv, Tanner Schmidt +1

We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotati…

cs.CV2019★ 7 cited

SENSE: a Shared Encoder Network for Scene-flow Estimation

Huaizu Jiang, Deqing Sun, Varun Jampani +3

We introduce a compact network for holistic scene flow estimation, called SENSE, which shares common encoder features among four closely-related tasks: optical flow estimation, dis…

cs.RO2019

miniSAM: A Flexible Factor Graph Non-linear Least Squares Optimization Framework

Jing Dong, Zhaoyang Lv

Many problems in computer vision and robotics can be phrased as non-linear least squares optimization problems represented by factor graphs, for example, simultaneous localization…

cs.LG2019★ 17 cited

Multi-class Classification without Multi-class Labels

Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser +2

This work presents a new strategy for multi-class classification that requires no class-specific labels, but instead leverages pairwise similarity between examples, which is a weak…